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* *The purpose of this light version threat model is to demonstrate how STRIDE can be applied to an ECG device. It is intended for readers learning system decomposition and threat modelling techniques. The example includes a simplified set of components, threats, and mitigations for educational purposes and is not intended to represent a comprehensive medical device cybersecurity assessment or any regulatory submission. **Assumption: This example models a typical ECG device, which may include network connectivity in a clinical environment. Trust Boundaries: Trust boundaries exist between the ECG device, hospital network, and external clinical systems. System Definition: ECG is the abbreviation for an Electrocardiogram. It is used to detect electrical activity of the heartbeat in the form of P wave, QRS complex and T wave to identify and diagnose irregularities in heartbeat. Electrodes are placed on patient’s limbs and chest to measure the electrical potentials. It translates tiny electrical signals into digital wave patterns. These waveforms are used by the doctors to evaluate the heart rhythm and check for cardiac damage. Components • Electrodes • Lead wires • Amplifier and filters • Analogue-to-Digital Converter (ADC) • Main processing unit • Display/printer • Local storage • Network interface (Ethernet/Wi-Fi/Bluetooth), if supported. Data Flow Diagram: Electrodes → Lead wires → Amplifier and filters → Analogue-to-Digital Converter (ADC) → Main processing unit → Display / Printer / Local storage / Network interface (if supported) |TRUST BOUNDARY|→ Electronic Health Record (EHR) / Clinical Information System 2. STRIDE Threats: Threats Description Spoofing in general ** - Spoofing is the act of impersonating a legitimate user, device, or system to gain unauthorized access to resources or services. Violates authentication. * Spoofing in ECG * - An attacker may impersonate an authorized clinician, connected medical device, or trusted clinical system to gain unauthoriz

I run a music metadata API as a solo developer. Under it sits a catalog of 90M+ recordings aggregated from six platforms: Spotify, Apple Music, Tidal, Beatport, Discogs, and MusicBrainz. The core job is cross-referencing: take whatever you know about a track (an ISRC, a platform ID, or a messy "artist + title" string from a DJ export) and resolve it to one canonical recording with everything else attached. When I started, I assumed this was mostly a plumbing problem. Every platform has an API, recordings have a standard identifier, join on it, done. Almost none of that survived contact with real data. This post is the parts I had to learn the hard way: why one song legitimately carries many ISRCs, how fuzzy matching on artist and title actually has to work, why recording-to-composition mapping is many-to-many in both directions, and the failure modes I now check for routinely. The ISRC almost solves it The ISRC (International Standard Recording Code) is a 12-character identifier for a specific recording. Daft Punk's "One More Time" is GBDUW0000053 : country prefix GB , registrant code DUW , year 00 , then a designation number. Every commercially released recording is supposed to have one, and most platforms expose it. So the naive architecture writes itself: one isrc column on the track table, join all six platforms on it, ship. That was my first schema, and it was wrong in a way that took a while to surface. Labels mint a fresh ISRC for every commercial variant of a recording. The radio edit gets one. The extended mix gets one. The 2001 release and the anniversary remaster get different ones. A reissue through a new distributor often gets one even when the audio is bit-identical. Regional releases sometimes get their own. None of this is an error; it is how the system is designed to work, because each of those is a distinct commercial product even when it is the same performance. The consequence: one canonical recording legitimately carries many ISRCs, and differen

A growing collection of pocket-size gadgets lets you easily make recordings and extract info from them. Here are our favorites.

Here’s when some of this year’s most anticipated astronomical events will happen and the best way to observe them.

The Series B is the company's second fundraise since it last raised capital in 2020. In that time, it has increased its ARR by 10x to $60 million.

Having a platform is not enough; the real challenge is ensuring that it is understandable, usable, and actually adopted by its users. A capability is done when it can be reliably used by others. To evaluate progress, you can ask yourself “Is this being used?” and “Does it reduce friction for users?” This can help align development work with actual user value rather than delivery, By Ben Linders

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I Replaced kube-proxy with eBPF in Production (And Why My Monitoring Went Blind for 6...

Whether you’re charging overnight at home or on the go, these are the fastest, most dependable Android chargers we’ve tested.

There are many companies manufacturing adversarial clothing designed to confuse facial recognition systems. It’s a cool idea, but I worry that it’s mostly security theater: “Our patterns play with that chaos, confuse algorithms and make it way harder to pin you down,” he said. Bell, however, said “none of these products are tried and tested, and a lot of these surveillance technologies can deal with a little resistance … [but] even if the designs don’t necessarily work perfectly, fashion is also a visible sign of resistance. “This is consumers collectively coming together to make a visible statement.”...

Zulfi Alam thinks his team doesn’t need to “prove” they engineered a new state of matter in their bid to reinvent computing. Science would disagree.

Samsung dared to bring 6K to the world of gaming monitors before our PCs were truly ready for it.

Shark’s new PowerDetect Speed is lighter and more affordable than the original, but still managed to ace most of my vacuum tests.

These services deliver freshly roasted, delicious coffee picks right to your door—each with its own twist.

Parte 13 de la serie Fitz . Se abre el capítulo del frontend: Fitz compila componentes .fitzv a WebAssembly, y este es el dev loop que hace que editarlos se sienta instantáneo — la misma experiencia "guardar y verlo" que te da Vite, sobre un lenguaje que compila a binario nativo. El setup: un lenguaje compilado con frontend Fitz es un lenguaje compilado — HTTP, async, Postgres, JWT viven en la sintaxis y emite un binario nativo vía Rust. La historia del frontend es un formato de componentes single-file, .fitzv (state + events + <template> , al estilo Vue/Svelte), que compila a WebAssembly : fitz build --bin web --target wasm-client # → target/wasm/web/{web.js, web_bg.wasm} Sin npm install , sin config de bundler, sin framework externo — el componente se vuelve un bundle WASM autocontenido (el demo del contador pesa 11.4 KB gzipped). Acá viene la objeción refleja: compilado = feedback lento . Editás, esperás una compilación entera, refrescás el browser a mano. Es lo opuesto a lo que un loop de frontend debería sentirse. Por eso Fitz tiene fitz dev . El loop Apuntá fitz dev a un bin wasm-client y deja de ser un compilador para ser un dev server: fitz dev # sirve en http://127.0.0.1:1234/ Qué hace: Rebuild incremental con wasm-pack --dev (sin wasm-opt ), reusando un crate estable así la cache de cargo queda caliente — el primer build compila las deps, cada save siguiente es de ~1-2 segundos . Un dev server que sirve el root de tu proyecto como python -m http.server : tu index.html , tu CSS, el bundle en target/wasm/<bin>/ . ¿Sin index.html ? Genera uno mínimo en el punto de mount . Auto-refresh del browser por WebSocket : guardás un .fitzv / .fitz / fitz.toml y la página se recarga sola. Sin F5 a mano. Guardás, y ~2 segundos después el browser muestra el cambio. En un lenguaje compilado. El detalle que importa: el state sobrevive el reload La mayoría de los hot-reload pierden tu estado en un reload completo — ibas tres clicks adentro de un contador, editás el template,

Cuando el objetivo es algo tan delicado como un diagnóstico asistido, confiar en un único modelo es arriesgado. FeliniAI usa tres pipelines complementarios que se refuerzan entre sí, igual que un veterinario combina lo que ve, lo que mide y lo que sabe. Pipeline 1 — Visión: MobileNetV2 Una CNN MobileNetV2 (PyTorch, transfer learning) clasifica imágenes de la piel/pelaje del gato en categorías visuales. Elegí MobileNetV2 por su equilibrio entre precisión y ligereza: corre rápido en CPU, lo que mantiene la inferencia por debajo de 1 segundo. Alcanza un 93,4% de accuracy visual . Pipeline 2 — Clínico: XGBoost + ICADA El núcleo del sistema es un clasificador XGBoost que trabaja sobre 33 features clínicas derivadas de los criterios ICADA (los criterios estandarizados de dermatitis atópica felina): estacionalidad, distribución de las lesiones, prurito, respuesta a tratamientos previos. Sobre un dataset de 8.000 casos , este módulo logra un F1 macro de 0.9675 en validación cruzada 5-fold. La búsqueda de hiperparámetros se hizo con Optuna y la explicabilidad con SHAP. Pipeline 3 — LLM: la síntesis Finalmente, un LLM ( Llama 3.3 70B vía Groq ) integra las salidas de los dos modelos anteriores y las traduce en una recomendación legible: qué tipo de alergia es más probable, con qué confianza y qué pasos sugerir. El LLM no diagnostica solo: orquesta y comunica lo que han calculado los modelos especializados. Por qué tres pipelines y no uno Porque cada uno cubre el punto ciego del otro. La visión capta lo que una foto muestra pero un cuestionario no; el modelo clínico capta el historial que una foto no puede mostrar; el LLM convierte ambos en algo accionable. Es un patrón de ensemble heterogéneo aplicado a datos de naturaleza distinta. Resultados F1 macro (clínico): 0.9675 , accuracy 0.9909. Accuracy visual: 93,4%. 4 tipos de alergia, 33 features clínicas, <1s de inferencia. Qué aprendí Que en dominios sensibles, la arquitectura correcta no es "el modelo más grande", sino varios

ChatGPT introduces improved GPT-5.6 Sol with better accuracy and consistency, plus expanded access for free users and unlimited everyday chats with GPT-5.6 Luna.

TL;DR — A Fitz LiveViews component is a single .fitzv file. The interesting part: the same file compiles to two different targets with no rewrite. Server-rendered (SSR) — the server holds the state, renders HTML, and patches the browser over a WebSocket; best for shared, DB-driven, multi-user state. Client-WASM — the same component compiles to WebAssembly and runs entirely in the browser; best for offline, zero-round-trip widgets. This post builds a counter and ships it both ways. (Part 2 of the FitzLiveViews series — start here if you missed part 1.) In part 1 I made the pitch: real-time UI in one language, no JavaScript build. Now let's build something and ship it two ways from the same source. The component Here's a counter as a single-file component ( .fitzv ) — state, events, template, style: component Counter { state { count: Int = 0 } event increment() { count = count + 1 } event decrement() { count = count - 1 } event reset() { count = 0 } <template> <div id= "counter-app" > <p> Count: {count} </p> <button @ click= "increment" > +1 </button> <button @ click= "decrement" > -1 </button> <button @ click= "reset" > Reset </button> </div> </template> <style scoped > #counter-app { padding : 1.5rem ; font-family : system-ui ; } button { padding : 0.5rem 1rem ; margin : 0 0.25rem ; } </style> } state is the reactive data. Each event handler mutates it directly — no setState , no reducers. <template> is real markup; {count} interpolates and auto-escapes. @click="increment" binds a DOM event to a handler. <style scoped> is CSS namespaced to this component. If you've written Vue or Svelte, this is familiar — the difference is what happens next. Target 1 — server-rendered (over a WebSocket) The SSR target is the default. The component runs on the server; a tiny main.fitz wires it into an HTTP route (first paint) and a WebSocket route (the live layer): from fitz_liveviews import html_response , live_layout , LiveFrame , diff_html , component , dispatch_component_events

Persistent Memory Layer for AI Agents Discussion | Link

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